{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ca2c4aea",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#第四周学习记录\" data-toc-modified-id=\"第四周学习记录-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>第四周学习记录</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据准备\" data-toc-modified-id=\"数据准备-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>数据准备</a></span></li></ul></li><li><span><a href=\"#Groupby\" data-toc-modified-id=\"Groupby-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Groupby</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "940ba9c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b92744c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>排名</td>\n",
       "      <td>排名变化</td>\n",
       "      <td>企业名称</td>\n",
       "      <td>价值（亿元人民币）</td>\n",
       "      <td>价值变化（亿元人民币）</td>\n",
       "      <td>国家</td>\n",
       "      <td>城市</td>\n",
       "      <td>行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>抖音</td>\n",
       "      <td>13400</td>\n",
       "      <td>-10050</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>社交媒体</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>SpaceX</td>\n",
       "      <td>8400</td>\n",
       "      <td>1680</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>航天</td>\n",
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       "      <th>3</th>\n",
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       "      <td>8000</td>\n",
       "      <td>-2010</td>\n",
       "      <td>中国</td>\n",
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       "      <td>金融科技</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Stripe</td>\n",
       "      <td>4100</td>\n",
       "      <td>-2210</td>\n",
       "      <td>美国</td>\n",
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       "      <td>95</td>\n",
       "      <td>-16</td>\n",
       "      <td>Impossible 食品</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>雷德伍德城</td>\n",
       "      <td>食品饮料</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>95</td>\n",
       "      <td>-16</td>\n",
       "      <td>微医</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>健康科技</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>58</td>\n",
       "      <td>蜂巢能源</td>\n",
       "      <td>460</td>\n",
       "      <td>190</td>\n",
       "      <td>中国</td>\n",
       "      <td>常州</td>\n",
       "      <td>新能源</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>99</td>\n",
       "      <td>-6</td>\n",
       "      <td>Better.com</td>\n",
       "      <td>460</td>\n",
       "      <td>60</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>金融科技</td>\n",
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       "      <th>101</th>\n",
       "      <td>99</td>\n",
       "      <td>-20</td>\n",
       "      <td>Automation Anywhere</td>\n",
       "      <td>460</td>\n",
       "      <td>-10</td>\n",
       "      <td>美国</td>\n",
       "      <td>圣何塞</td>\n",
       "      <td>人工智能</td>\n",
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      "text/plain": [
       "      0     1                    2          3            4   5      6     7\n",
       "0    排名  排名变化                 企业名称  价值（亿元人民币）  价值变化（亿元人民币）  国家     城市    行业\n",
       "1     1     0                   抖音      13400       -10050  中国     北京  社交媒体\n",
       "2     2     1               SpaceX       8400         1680  美国    洛杉矶    航天\n",
       "3     3    -1                 蚂蚁集团       8000        -2010  中国     杭州  金融科技\n",
       "4     4     0               Stripe       4100        -2210  美国    旧金山  金融科技\n",
       "..   ..   ...                  ...        ...          ...  ..    ...   ...\n",
       "97   95   -16        Impossible 食品        470            0  美国  雷德伍德城  食品饮料\n",
       "98   95   -16                   微医        470            0  中国     杭州  健康科技\n",
       "99   99    58                 蜂巢能源        460          190  中国     常州   新能源\n",
       "100  99    -6           Better.com        460           60  美国     纽约  金融科技\n",
       "101  99   -20  Automation Anywhere        460          -10  美国    圣何塞  人工智能\n",
       "\n",
       "[102 rows x 8 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hurun_独角兽 = pd.read_html('https://www.hurun.net/zh-CN/Info/Detail?num=L9SQPH9FKJB1')[-3]\n",
    "hurun_独角兽"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "978cf373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['排名', '排名变化', '企业名称', '价值（亿元人民币）', '价值变化（亿元人民币）', '国家', '城市', '行业']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hurun_独角兽[0:1].values.tolist()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f58d0250",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>中国</td>\n",
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       "      <td>健康科技</td>\n",
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       "      <th>99</th>\n",
       "      <td>99</td>\n",
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       "      <td>新能源</td>\n",
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       "      <th>100</th>\n",
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       "      <td>纽约</td>\n",
       "      <td>金融科技</td>\n",
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       "      <td>99</td>\n",
       "      <td>-20</td>\n",
       "      <td>Automation Anywhere</td>\n",
       "      <td>460</td>\n",
       "      <td>-10</td>\n",
       "      <td>美国</td>\n",
       "      <td>圣何塞</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
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       "<p>101 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "      0    1                    2      3       4   5      6     7\n",
       "1     1    0                   抖音  13400  -10050  中国     北京  社交媒体\n",
       "2     2    1               SpaceX   8400    1680  美国    洛杉矶    航天\n",
       "3     3   -1                 蚂蚁集团   8000   -2010  中国     杭州  金融科技\n",
       "4     4    0               Stripe   4100   -2210  美国    旧金山  金融科技\n",
       "5     5   11                Shein   4000    2680  中国     广州  电子商务\n",
       "..   ..  ...                  ...    ...     ...  ..    ...   ...\n",
       "97   95  -16        Impossible 食品    470       0  美国  雷德伍德城  食品饮料\n",
       "98   95  -16                   微医    470       0  中国     杭州  健康科技\n",
       "99   99   58                 蜂巢能源    460     190  中国     常州   新能源\n",
       "100  99   -6           Better.com    460      60  美国     纽约  金融科技\n",
       "101  99  -20  Automation Anywhere    460     -10  美国    圣何塞  人工智能\n",
       "\n",
       "[101 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hurun = hurun_独角兽[1:]\n",
    "df_hurun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fcb4c79",
   "metadata": {},
   "outputs": [
    {
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       "      <td>460</td>\n",
       "      <td>190</td>\n",
       "      <td>中国</td>\n",
       "      <td>常州</td>\n",
       "      <td>新能源</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>99</td>\n",
       "      <td>-6</td>\n",
       "      <td>Better.com</td>\n",
       "      <td>460</td>\n",
       "      <td>60</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>99</td>\n",
       "      <td>-20</td>\n",
       "      <td>Automation Anywhere</td>\n",
       "      <td>460</td>\n",
       "      <td>-10</td>\n",
       "      <td>美国</td>\n",
       "      <td>圣何塞</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     排名 排名变化                 企业名称 价值（亿元人民币） 价值变化（亿元人民币）  国家     城市    行业\n",
       "1     1    0                   抖音     13400      -10050  中国     北京  社交媒体\n",
       "2     2    1               SpaceX      8400        1680  美国    洛杉矶    航天\n",
       "3     3   -1                 蚂蚁集团      8000       -2010  中国     杭州  金融科技\n",
       "4     4    0               Stripe      4100       -2210  美国    旧金山  金融科技\n",
       "5     5   11                Shein      4000        2680  中国     广州  电子商务\n",
       "..   ..  ...                  ...       ...         ...  ..    ...   ...\n",
       "97   95  -16        Impossible 食品       470           0  美国  雷德伍德城  食品饮料\n",
       "98   95  -16                   微医       470           0  中国     杭州  健康科技\n",
       "99   99   58                 蜂巢能源       460         190  中国     常州   新能源\n",
       "100  99   -6           Better.com       460          60  美国     纽约  金融科技\n",
       "101  99  -20  Automation Anywhere       460         -10  美国    圣何塞  人工智能\n",
       "\n",
       "[101 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hurun.columns = hurun_独角兽[0:1].values.tolist()[0]\n",
    "df_hurun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56e101d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['中国', '美国', '马耳他', '英国', '澳大利亚', '印度', '瑞典', '印度尼西亚', '巴哈马', '土耳其',\n",
       "       '墨西哥', '瑞士', '韩国', '德国', '越南', '以色列'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hurun['国家'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12727936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hurun['国家'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2129b513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "美国       49\n",
       "中国       26\n",
       "英国        7\n",
       "印度        4\n",
       "瑞典        2\n",
       "印度尼西亚     2\n",
       "韩国        2\n",
       "马耳他       1\n",
       "澳大利亚      1\n",
       "巴哈马       1\n",
       "土耳其       1\n",
       "墨西哥       1\n",
       "瑞士        1\n",
       "德国        1\n",
       "越南        1\n",
       "以色列       1\n",
       "Name: 国家, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hurun['国家'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cc9ccc1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['北京', '洛杉矶', '杭州', '旧金山', '广州', '马耳他', '深圳', '伦敦', '悉尼', '芝加哥',\n",
       "       '班加罗尔', '哥德堡', '雅加达', '上海', '拿索', 'Novi', '费城', '香港', '沃尔瑟姆',\n",
       "       '伊斯坦布尔', '圣迭戈', '斯德哥尔摩', '纽约', 'Kebayoran Baru', '长沙', '无锡', '常州',\n",
       "       '爱莫利维尔', '宁波', '墨西哥城', 'Zug', '首尔', '圣何塞', '慕尼黑', '胡志明市', '内坦亚',\n",
       "       '孟买', '宿迁', '哈里斯堡', '帕洛阿尔托', '波士顿', '格兰岱尔市', '古尔冈', '雷德伍德城'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hurun['城市'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "60cf11a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hurun['城市'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53898ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "旧金山               23\n",
       "北京                 8\n",
       "纽约                 8\n",
       "伦敦                 7\n",
       "杭州                 3\n",
       "深圳                 3\n",
       "上海                 3\n",
       "帕洛阿尔托              2\n",
       "广州                 2\n",
       "洛杉矶                2\n",
       "首尔                 2\n",
       "圣何塞                2\n",
       "常州                 2\n",
       "波士顿                2\n",
       "班加罗尔               2\n",
       "芝加哥                2\n",
       "哈里斯堡               1\n",
       "宁波                 1\n",
       "孟买                 1\n",
       "内坦亚                1\n",
       "胡志明市               1\n",
       "慕尼黑                1\n",
       "格兰岱尔市              1\n",
       "古尔冈                1\n",
       "Zug                1\n",
       "墨西哥城               1\n",
       "宿迁                 1\n",
       "Kebayoran Baru     1\n",
       "爱莫利维尔              1\n",
       "无锡                 1\n",
       "长沙                 1\n",
       "斯德哥尔摩              1\n",
       "圣迭戈                1\n",
       "伊斯坦布尔              1\n",
       "沃尔瑟姆               1\n",
       "香港                 1\n",
       "费城                 1\n",
       "Novi               1\n",
       "拿索                 1\n",
       "雅加达                1\n",
       "哥德堡                1\n",
       "悉尼                 1\n",
       "马耳他                1\n",
       "雷德伍德城              1\n",
       "Name: 城市, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hurun['城市'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e3587f",
   "metadata": {},
   "source": [
    "# 第四周学习记录"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8dc209d",
   "metadata": {},
   "source": [
    "## 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d24609cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b81ff25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:04:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Li Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/22</td>\n",
       "      <td>0:04:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengqiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/5</td>\n",
       "      <td>0:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengmei Shen</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Chunpeng Lv</td>\n",
       "      <td>Male</td>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School      Grade            Name  Gender  Height  \\\n",
       "0    Shanghai Jiao Tong University   Freshman    Gaopeng Yang  Female   158.9   \n",
       "1                Peking University   Freshman  Changqiang You    Male   166.5   \n",
       "2    Shanghai Jiao Tong University     Senior         Mei Sun    Male   188.9   \n",
       "3                 Fudan University  Sophomore    Xiaojuan Sun  Female     NaN   \n",
       "4                 Fudan University  Sophomore     Gaojuan You    Male   174.0   \n",
       "..                             ...        ...             ...     ...     ...   \n",
       "195               Fudan University     Junior    Xiaojuan Sun  Female   153.9   \n",
       "196            Tsinghua University     Senior         Li Zhao  Female   160.9   \n",
       "197  Shanghai Jiao Tong University     Senior  Chengqiang Chu  Female   153.9   \n",
       "198  Shanghai Jiao Tong University     Senior   Chengmei Shen    Male   175.3   \n",
       "199            Tsinghua University  Sophomore     Chunpeng Lv    Male   155.7   \n",
       "\n",
       "     Weight Transfer  Test_Number   Test_Date Time_Record  \n",
       "0      46.0        N            1   2019/10/5     0:04:34  \n",
       "1      70.0        N            1    2019/9/4     0:04:20  \n",
       "2      89.0        N            2   2019/9/12     0:05:22  \n",
       "3      41.0        N            2    2020/1/3     0:04:08  \n",
       "4      74.0        N            2   2019/11/6     0:05:22  \n",
       "..      ...      ...          ...         ...         ...  \n",
       "195    46.0        N            2  2019/10/17     0:04:31  \n",
       "196    50.0        N            3   2019/9/22     0:04:03  \n",
       "197    45.0        N            1    2020/1/5     0:04:48  \n",
       "198    71.0        N            2    2020/1/7     0:04:58  \n",
       "199    51.0        N            1   2019/11/6     0:05:05  \n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/learn_pandas.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52e0fbb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          School      Grade            Name  Gender  Height  \\\n",
       "0  Shanghai Jiao Tong University   Freshman    Gaopeng Yang  Female   158.9   \n",
       "1              Peking University   Freshman  Changqiang You    Male   166.5   \n",
       "2  Shanghai Jiao Tong University     Senior         Mei Sun    Male   188.9   \n",
       "3               Fudan University  Sophomore    Xiaojuan Sun  Female     NaN   \n",
       "4               Fudan University  Sophomore     Gaojuan You    Male   174.0   \n",
       "\n",
       "   Weight Transfer  Test_Number  Test_Date Time_Record  \n",
       "0    46.0        N            1  2019/10/5     0:04:34  \n",
       "1    70.0        N            1   2019/9/4     0:04:20  \n",
       "2    89.0        N            2  2019/9/12     0:05:22  \n",
       "3    41.0        N            2   2020/1/3     0:04:08  \n",
       "4    74.0        N            2  2019/11/6     0:05:22  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "519dbe73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:04:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Li Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/22</td>\n",
       "      <td>0:04:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengqiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/5</td>\n",
       "      <td>0:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengmei Shen</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Chunpeng Lv</td>\n",
       "      <td>Male</td>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School      Grade            Name  Gender  Height  \\\n",
       "195               Fudan University     Junior    Xiaojuan Sun  Female   153.9   \n",
       "196            Tsinghua University     Senior         Li Zhao  Female   160.9   \n",
       "197  Shanghai Jiao Tong University     Senior  Chengqiang Chu  Female   153.9   \n",
       "198  Shanghai Jiao Tong University     Senior   Chengmei Shen    Male   175.3   \n",
       "199            Tsinghua University  Sophomore     Chunpeng Lv    Male   155.7   \n",
       "\n",
       "     Weight Transfer  Test_Number   Test_Date Time_Record  \n",
       "195    46.0        N            2  2019/10/17     0:04:31  \n",
       "196    50.0        N            3   2019/9/22     0:04:03  \n",
       "197    45.0        N            1    2020/1/5     0:04:48  \n",
       "198    71.0        N            2    2020/1/7     0:04:58  \n",
       "199    51.0        N            1   2019/11/6     0:05:05  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ff7edea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   School       200 non-null    object \n",
      " 1   Grade        200 non-null    object \n",
      " 2   Name         200 non-null    object \n",
      " 3   Gender       200 non-null    object \n",
      " 4   Height       183 non-null    float64\n",
      " 5   Weight       189 non-null    float64\n",
      " 6   Transfer     188 non-null    object \n",
      " 7   Test_Number  200 non-null    int64  \n",
      " 8   Test_Date    200 non-null    object \n",
      " 9   Time_Record  200 non-null    object \n",
      "dtypes: float64(2), int64(1), object(7)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# object是可分类项，float是可计算项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "58f806bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Shanghai Jiao Tong University', 'Peking University',\n",
       "       'Fudan University', 'Tsinghua University'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['School'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da0bc23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gaopeng Yang', 'Changqiang You', 'Mei Sun', 'Xiaojuan Sun',\n",
       "       'Gaojuan You', 'Xiaoli Qian', 'Qiang Chu', 'Gaoqiang Qian',\n",
       "       'Changli Zhang', 'Juan Xu', 'Xiaopeng Zhou', 'Xiaoquan Lv',\n",
       "       'Peng You', 'Yanfeng Qian', 'Xiaomei Zhou', 'Changqiang Yang',\n",
       "       'Xiaoqiang Qin', 'Peng Wang', 'Xiaofeng Sun', 'Changjuan You',\n",
       "       'Xiaopeng Shen', 'Changqiang Sun', 'Qiang Zheng', 'Chunmei You',\n",
       "       'Xiaopeng Chu', 'Yanli You', 'Qiang Sun', 'Gaoqiang Qin',\n",
       "       'Changmei Xu', 'Changli Lv', 'Feng Zheng', 'Gaopeng Shi',\n",
       "       'Yanjuan Han', 'Li Wu', 'Gaoli Zhao', 'Xiaojuan Qin',\n",
       "       'Xiaoquan Zhang', 'Qiang Han', 'Chengpeng Zheng', 'Li Wang',\n",
       "       'Chunqiang Chu', 'Mei Zhang', 'Gaoli Feng', 'Yanqiang Feng',\n",
       "       'Quan Chu', 'Feng Zhou', 'Peng Wu', 'Mei Xu', 'Gaomei Lv',\n",
       "       'Xiaoli Wang', 'Chengquan Chu', 'Chunli Lv', 'Chengli You',\n",
       "       'Xiaojuan Chu', 'Chengquan Zhang', 'Qiang Lv', 'Changquan Chu',\n",
       "       'Gaoli Xu', 'Yanpeng Lv', 'Xiaopeng Qin', 'Xiaoli Xu',\n",
       "       'Gaofeng Zhao', 'Yanmei Yang', 'Chengpeng Zhou', 'Gaoquan Sun',\n",
       "       'Chengqiang Lv', 'Chunquan Xu', 'Yanquan Wang', 'Feng Han',\n",
       "       'Gaoquan Zhou', 'Feng Wang', 'Yanli Qin', 'Qiang You',\n",
       "       'Yanquan Lv', 'Gaopeng Qin', 'Li Xu', 'Changmei Sun',\n",
       "       'Yanli Zhang', 'Changfeng Lv', 'Yanjuan Lv', 'Li Chu', 'Feng Yang',\n",
       "       'Xiaopeng Han', 'Gaojuan Zhao', 'Gaoqiang Zhou', 'Yanfeng Han',\n",
       "       'Juan Zhao', 'Feng Zhao', 'Yanli Wang', 'Changmei Feng',\n",
       "       'Changpeng Zhao', 'Xiaofeng Shi', 'Xiaoli Zhou', 'Chengli Zhao',\n",
       "       'Mei Chen', 'Xiaopeng Lv', 'Qiang Shi', 'Xiaojuan Zhao',\n",
       "       'Yanqiang Xu', 'Chunpeng Lv', 'Xiaomei Shi', 'Gaoquan Xu',\n",
       "       'Chunjuan Xu', 'Changjuan Xu', 'Xiaopeng Zhao', 'Gaofeng Sun',\n",
       "       'Chunli Zhao', 'Peng Zhang', 'Peng Han', 'Xiaoquan Sun',\n",
       "       'Chunpeng Shi', 'Juan You', 'Changquan Han', 'Xiaofeng You',\n",
       "       'Juan Zhang', 'Mei Feng', 'Chengpeng Qian', 'Chunpeng Qian',\n",
       "       'Gaojuan Qin', 'Changqiang Qian', 'Li Lv', 'Chengquan Shi',\n",
       "       'Xiaojuan Qian', 'Qiang Zhou', 'Qiang Zhang', 'Chunmei Shi',\n",
       "       'Xiaoli Chu', 'Quan Xu', 'Gaoquan Chu', 'Xiaomei Yang',\n",
       "       'Xiaofeng Qian', 'Chengpeng You', 'Feng Qian', 'Chengli Sun',\n",
       "       'Changmei Lv', 'Yanpeng Han', 'Chunmei Han', 'Juan Qin',\n",
       "       'Xiaoli Lv', 'Chengqiang Zhang', 'Chengpeng Zhao', 'Chunfeng Zhao',\n",
       "       'Quan Qian', 'Chengjuan Zhang', 'Gaoquan Shen', 'Qiang Wang',\n",
       "       'Xiaopeng Qian', 'Xiaoqiang Feng', 'Gaoli Wu', 'Chengquan Qin',\n",
       "       'Li Sun', 'Xiaofeng Zhang', 'Quan Zhao', 'Gaojuan Qian',\n",
       "       'Xiaopeng Sun', 'Li Qin', 'Mei Zheng', 'Yanjuan You',\n",
       "       'Xiaoqiang Qian', 'Xiaofeng Zhao', 'Qiang Feng', 'Chunmei Wang',\n",
       "       'Yanjuan Zhao', 'Chunjuan Zhang', 'Changli Qin', 'Gaojuan Wang',\n",
       "       'Yanmei Qian', 'Li Zhao', 'Chengqiang Chu', 'Chengmei Shen'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55f08142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Freshman', 'Senior', 'Sophomore', 'Junior'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Grade'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "18446e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163.21803278688526"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Height'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09229681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tsinghua University              69\n",
       "Shanghai Jiao Tong University    57\n",
       "Fudan University                 40\n",
       "Peking University                34\n",
       "Name: School, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['School'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fb7046",
   "metadata": {},
   "source": [
    "# Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "70e8f4c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Fudan University</th>\n",
       "      <th>Female</th>\n",
       "      <td>158.776923</td>\n",
       "      <td>47.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>174.212500</td>\n",
       "      <td>72.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Peking University</th>\n",
       "      <th>Female</th>\n",
       "      <td>158.666667</td>\n",
       "      <td>46.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>172.030000</td>\n",
       "      <td>73.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Shanghai Jiao Tong University</th>\n",
       "      <th>Female</th>\n",
       "      <td>159.122500</td>\n",
       "      <td>48.513514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>176.760000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tsinghua University</th>\n",
       "      <th>Female</th>\n",
       "      <td>159.753333</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>171.638889</td>\n",
       "      <td>69.947368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Height     Weight\n",
       "School                        Gender                       \n",
       "Fudan University              Female  158.776923  47.900000\n",
       "                              Male    174.212500  72.300000\n",
       "Peking University             Female  158.666667  46.650000\n",
       "                              Male    172.030000  73.700000\n",
       "Shanghai Jiao Tong University Female  159.122500  48.513514\n",
       "                              Male    176.760000  76.000000\n",
       "Tsinghua University           Female  159.753333  48.000000\n",
       "                              Male    171.638889  69.947368"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['School','Gender']).agg({'Height':'mean','Weight':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0b843dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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